{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"_o8B3fQbY-LJ"},"outputs":[],"source":["!pip install torch bitsandbytes datasets==2.13.1 scipy\n","!pip install accelerate\n","!pip install transformers\n","!pip install peft\n","!pip install trl"],"id":"_o8B3fQbY-LJ"},{"cell_type":"code","execution_count":null,"metadata":{"id":"c7pH7JN4ZVBZ"},"outputs":[],"source":["import argparse\n","import bitsandbytes as bnb\n","from datasets import load_dataset\n","from functools import partial\n","import os\n","from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training, AutoPeftModelForCausalLM\n","import torch\n","from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed, Trainer, TrainingArguments, BitsAndBytesConfig, \\\n","    DataCollatorForLanguageModeling, Trainer, TrainingArguments\n","from datasets import load_dataset"],"id":"c7pH7JN4ZVBZ"},{"cell_type":"markdown","metadata":{"id":"kwttQTkcIYra"},"source":["# Important Functions"],"id":"kwttQTkcIYra"},{"cell_type":"code","execution_count":null,"metadata":{"id":"ztW4QY4mJEBp"},"outputs":[],"source":[],"id":"ztW4QY4mJEBp"},{"cell_type":"code","execution_count":null,"metadata":{"id":"yqE7RPE0jfxV"},"outputs":[],"source":["def train(model, tokenizer, dataset, output_dir):\n","    # Apply preprocessing to the model to prepare it by\n","    # 1 - Enabling gradient checkpointing to reduce memory usage during fine-tuning\n","    model.gradient_checkpointing_enable()\n","\n","    # 2 - Using the prepare_model_for_kbit_training method from PEFT\n","    model = prepare_model_for_kbit_training(model)\n","\n","    # Get lora module names\n","    modules = find_all_linear_names(model)\n","    print(\"bunlar \"+  str(modules) )\n","\n","    # Create PEFT config for these modules and wrap the model to PEFT\n","    peft_config = create_peft_config(modules)\n","    model = get_peft_model(model, peft_config)\n","\n","    # Print information about the percentage of trainable parameters\n","    print_trainable_parameters(model)\n","\n","    # Training parameters\n","    trainer = Trainer(\n","        model=model,\n","        train_dataset=dataset,\n","        args=TrainingArguments(\n","            per_device_train_batch_size=1,\n","            gradient_accumulation_steps=4,\n","            warmup_steps=2,\n","            max_steps=20,\n","            learning_rate=2e-4,\n","            fp16=True,\n","            logging_steps=1,\n","            output_dir=\"outputs\",\n","            optim=\"paged_adamw_8bit\",\n","        ),\n","        data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n","    )\n","\n","    model.config.use_cache = False  # re-enable for inference to speed up predictions for similar inputs\n","\n","    ### SOURCE https://github.com/artidoro/qlora/blob/main/qlora.py\n","    # Verifying the datatypes before training\n","\n","    dtypes = {}\n","    for _, p in model.named_parameters():\n","        dtype = p.dtype\n","        if dtype not in dtypes: dtypes[dtype] = 0\n","        dtypes[dtype] += p.numel()\n","    total = 0\n","    for k, v in dtypes.items(): total+= v\n","    for k, v in dtypes.items():\n","        print(k, v, v/total)\n","\n","    do_train = True\n","\n","    # Launch training\n","    print(\"Training...\")\n","\n","    if do_train:\n","        train_result = trainer.train()\n","        metrics = train_result.metrics\n","        trainer.log_metrics(\"train\", metrics)\n","        trainer.save_metrics(\"train\", metrics)\n","        trainer.save_state()\n","        print(metrics)\n","\n","    ###\n","\n","    # Saving model\n","    print(\"Saving last checkpoint of the model...\")\n","    os.makedirs(output_dir, exist_ok=True)\n","    trainer.model.save_pretrained(output_dir)\n","\n","    # Free memory for merging weights\n","    del model\n","    del trainer\n","    torch.cuda.empty_cache()\n","\n","\n","\n","def create_prompt_formats_SNLI(sample):\n","    \"\"\"\n","    Format various fields of the sample ('premise',\t'hypothesis',\tlabel')\n","    Then concatenate them using two newline characters\n","    :param sample: Sample dictionnary\n","    \"\"\"\n","    INTRO_BLURB = \"Below is an NLI instruction that describes a NLI task. .\"\n","    INSTRUCTION_KEY = \"### Instruction:\"\n","    INPUT_KEY = \"### Input:\"\n","    RESPONSE_KEY = \"### Response:\"\n","    END_KEY = \"### End\"\n","\n","    blurb = f\"{INTRO_BLURB}\"\n","    instruction = f\"{INSTRUCTION_KEY}\\n Detect whether the given “hypothesis” logically follows from the 'premise'\"\n","    input_context = f\"{INPUT_KEY}\\n Hypothesis: {sample['hypothesis']}, Premise:{sample['premise']}  \"\n","    response = f\"{RESPONSE_KEY}\\n{mapp[sample['label']]}\"\n","    end = f\"{END_KEY}\"\n","\n","    parts = [part for part in [blurb, instruction, input_context, response, end] if part]\n","\n","    formatted_prompt = \"\\n\\n\".join(parts)\n","\n","    sample[\"text\"] = formatted_prompt\n","    return sample\n","\n","# SOURCE https://github.com/databrickslabs/dolly/blob/master/training/trainer.py\n","def get_max_length(model):\n","    conf = model.config\n","    max_length = None\n","    for length_setting in [\"n_positions\", \"max_position_embeddings\", \"seq_length\"]:\n","        max_length = getattr(model.config, length_setting, None)\n","        if max_length:\n","            print(f\"Found max lenth: {max_length}\")\n","            break\n","    if not max_length:\n","        max_length = 1024\n","        print(f\"Using default max length: {max_length}\")\n","    return max_length\n","\n","\n","def preprocess_batch(batch, tokenizer, max_length):\n","    \"\"\"\n","    Tokenizing a batch\n","    \"\"\"\n","    return tokenizer(\n","        batch[\"text\"],\n","        max_length=max_length,\n","        truncation=True,\n","    )\n","\n","\n","# SOURCE https://github.com/databrickslabs/dolly/blob/master/training/trainer.py\n","def preprocess_dataset(tokenizer: AutoTokenizer, max_length: int, seed, dataset):\n","    \"\"\"Format & tokenize it so it is ready for training\n","    :param tokenizer (AutoTokenizer): Model Tokenizer\n","    :param max_length (int): Maximum number of tokens to emit from tokenizer\n","    \"\"\"\n","\n","    # Add prompt to each sample\n","    print(\"Preprocessing dataset...\")\n","    dataset = dataset.map(create_prompt_formats_SNLI)#, batched=True)\n","\n","    # Apply preprocessing to each batch of the dataset & and remove 'instruction', 'context', 'response', 'category' fields\n","    _preprocessing_function = partial(preprocess_batch, max_length=max_length, tokenizer=tokenizer)\n","    dataset = dataset.map(\n","        _preprocessing_function,\n","        batched=True,\n","        remove_columns=['premise', 'hypothesis', 'label', '__index_level_0__','text'],\n","    )\n","\n","    # Filter out samples that have input_ids exceeding max_length\n","    dataset = dataset.filter(lambda sample: len(sample[\"input_ids\"]) < max_length)\n","\n","    # Shuffle dataset\n","    dataset = dataset.shuffle(seed=seed)\n","\n","    return dataset\n","\n","def create_bnb_config():\n","    bnb_config = BitsAndBytesConfig(\n","        load_in_4bit=True,\n","        bnb_4bit_use_double_quant=True,\n","        bnb_4bit_quant_type=\"nf4\",\n","        bnb_4bit_compute_dtype=torch.bfloat16,\n","    )\n","\n","    return bnb_config\n","\n","def create_peft_config(modules):\n","    \"\"\"\n","    Create Parameter-Efficient Fine-Tuning config for your model\n","    :param modules: Names of the modules to apply Lora to\n","    \"\"\"\n","    config = LoraConfig(\n","        r=16,  # dimension of the updated matrices\n","        lora_alpha=64,  # parameter for scaling\n","        target_modules=modules,\n","        lora_dropout=0.1,  # dropout probability for layers\n","        bias=\"none\",\n","        task_type=\"CAUSAL_LM\",\n","    )\n","    return config\n","\n","def find_all_linear_names(model):\n","    # the return of this function will be passed to LoraConfig(target_modules=...)\n","    cls = bnb.nn.Linear4bit #if args.bits == 4 else (bnb.nn.Linear8bitLt if args.bits == 8 else torch.nn.Linear)\n","    lora_module_names = set()\n","    for name, module in model.named_modules():\n","        if isinstance(module, cls):\n","            names = name.split('.')\n","            lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n","    print(\"neymis bu lora names...\"+str(lora_module_names))\n","    if 'lm_head' in lora_module_names:  # needed for 16-bit\n","        lora_module_names.remove('lm_head')\n","    return list(lora_module_names)\n","\n","def print_trainable_parameters(model, use_4bit=False):\n","    \"\"\"\n","    Prints the number of trainable parameters in the model.\n","    \"\"\"\n","    trainable_params = 0\n","    all_param = 0\n","    for _, param in model.named_parameters():\n","        num_params = param.numel()\n","        # if using DS Zero 3 and the weights are initialized empty\n","        if num_params == 0 and hasattr(param, \"ds_numel\"):\n","            num_params = param.ds_numel\n","\n","        all_param += num_params\n","        if param.requires_grad:\n","            trainable_params += num_params\n","    if use_4bit:\n","        trainable_params /= 2\n","    print(\n","        f\"all params: {all_param:,d} || trainable params: {trainable_params:,d} || trainable%: {100 * trainable_params / all_param}\"\n","    )\n","def load_model(model_name, bnb_config):\n","    n_gpus = torch.cuda.device_count()\n","    max_memory = f'{40960}MB'\n","    model = AutoModelForCausalLM.from_pretrained(\n","        model_name,\n","        quantization_config=bnb_config,\n","        device_map=\"auto\", # dispatch efficiently the model on the available ressources\n","        max_memory = {i: max_memory for i in range(n_gpus)},\n","    )\n","    tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)\n","    # Needed for LLaMA tokenizer\n","    tokenizer.pad_token = tokenizer.eos_token\n","    return model, tokenizer\n"],"id":"yqE7RPE0jfxV"},{"cell_type":"markdown","metadata":{"id":"TAkE_zkbInxJ"},"source":["# Load Dataset"],"id":"TAkE_zkbInxJ"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":329,"referenced_widgets":["25122e26d6324b1eae6e7412471d2b36","0a0e3d920d514aba8f80b47ec2b6a795","8dda302f8fba4de4a778ddd7f10d0ae5","04dfeeed06f1459085de8f95c67fbb4a","027d586d1dae403791144d92d2e911a2","b53d49f4b9ad432ba1a20a8d5b9f072c","5013b4e687654346a06ae2fe9b2362d8","8b446be9c9114737bc09c00eeb27012c","a37dc40029484b67b5f3ca0ca0d7a3c4","79fd7073cab44e3e954401855aa433f9","53ebaa82be82498fa13836c85edafeca","6f8e624a70a6434688b737c13587419c","d049b732bf8d46c7b689f2532b5adb6e","5aa127770a1140f397ac27708bff9f2a","649ade4fc3f04c799fd9b932644ee30f","5d0dc831d363462a8aa89cb150685f75","954fa62ed92b4f15b40f94f27c6fbd81","d1d3cb7f445a4bbfa90dc39cdd13a2d1","2738cecb27b044eaa48af45a34eb1788","9b7cd9d00faa425a9770f77702e95fbb","2b14dcefbe9c4aa8a6a6053ba28cd518","6862da55fc70449ab7700fc9983b55fa","e25c157b0d204d7385fa26e015093280","0c57d9175fd844399591f021540d73b1","16a4ad8c03034f59958db6f8b912dd72","737c5343e74740a1a6de86801c682c6b","8dc6b02ce6374e78babaea4d36c73801","62eb488a79a94e16add0b47630853dd6","2d3ff53ead634cba8d595088e4df1fe6","f19fb5f6e759451cb9e1967554910798","4d67bc49ad2b43e987b4f300c87b9d96","323291a5b4734bd9a2e527e8ccbe13bc","2053d3877abb427ea5f85ba52498e9ae","d10badda20e3457f990d661464242e09","d9a4c47d29314c059c99f5500b1d224d","fb64aceb1a2b490bb131a362b24c71b5","a1d6163741a848b2862c32dab686a600","ba5c611dd7c5426384aa50f60a423866","70fde323b5df4d28bf7d01539e69ff59","b15952e3237c41568b6e97841034dad3","317115aa59fb44ba8e43852a7adcd53f","4791cccf834642b48e4ef6c706f3be46","14fef1d63a3f4439ab9213e4faafe5bd","cade06982e354ceab72aa4a46d9bd0c5","1a5c9a669de042c48d8a64b93223ffab","232f6bf05987494ab85fb107e3418772","aee141a91e664d83af21947fb47ece9c","be947eb2a0a4483cba94de324394c23d","a9425f0b32fb45108e18986fc1c935b7","cee679a828f74c29a4a445fe692b86f1","bcf5538087ba4f09bd6069377054af9d","7891a069ac914828a9a613b414abe6d3","c79e69dfcef24bee9c4c61e25c3d68e8","3818a8b9e23543389f6c87cf9a4efe1e","2f19e4b8152b4276948bfd48c887e03e","cc650f5f06364d90930a9a79de3ca218","08e90cc4b3d4494da2c936ea5952dd90","9748846fdd654ee8b31478ef5d3cbde8","217df75d6538474ba7e99ce03787510c","4b8aa6a6400e4573a6ed8c58503ebb7a","072d8ecdb72b48a78c45b6fbb9ba64d9","d4da20a1ebb44dcda2f63f09ff4e3be5","9ee32802c99f4b039d1457d9fd1a18e1","05a592417ae34cbaa7f16807845f2683","c50948c82c694a8286192a36f092d3da","77d0fe146d434a7ca3ca27c4f13ebdfe","4a819064fa384ecf919bb9449d66de83","1d0d19c41a4f48909e9358d66cb5afce","bb4562ba3bc549dba541a70bc34c9e83","b648cb2cefb24e9bae05721888d2b1a3","0b2d11aa2c074cdcb8568f71154b7b26","4e9abdab43dd48cc8a8029969da4ecbf","88927ed3c84f4a6aba5cd0721b341eda","b8ad50c591434cdfa87877d0ac34df3f","5d765690f916443b87e85d36698cb671","82db8b4adff745eba03718b430991f07","abdd7378f06d40718ef71019f233db19","28da8980613145c39b658eb82acba1dd","bb20a1bc56584a3db322057f32a3e30d","a12826b6caec4a2f9c808023a6cdb7cb","00af9213a8604d75aafd28d2c46ddeef","7bb0849c11834aeeb59cdcb5601cb616","c9a1bb8e92f245f1b5b28f90e0a4e1a3","67dca26beebd4632ad1005c1acae124b","68c901ed26c04f77a743c38e6afbda48","7feaaae6c77b483eac1e95458cefbddb","dbbd1cb4c60f4b6f86a43e949a49f8cd","66c4b33b5ec84fd5847e84f86da90b48"]},"executionInfo":{"elapsed":40180,"status":"ok","timestamp":1692374640514,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"GwkbBNtw2Q1D","outputId":"1fef2d80-168a-4761-c07c-dfed3040b3e8"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/3.82k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"25122e26d6324b1eae6e7412471d2b36"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/1.90k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6f8e624a70a6434688b737c13587419c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/14.1k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e25c157b0d204d7385fa26e015093280"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset snli/plain_text to /root/.cache/huggingface/datasets/snli/plain_text/1.0.0/1f60b67533b65ae0275561ff7828aad5ee4282d0e6f844fd148d05d3c6ea251b...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.93k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d10badda20e3457f990d661464242e09"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.26M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1a5c9a669de042c48d8a64b93223ffab"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/65.9M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cc650f5f06364d90930a9a79de3ca218"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.26M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4a819064fa384ecf919bb9449d66de83"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset snli downloaded and prepared to /root/.cache/huggingface/datasets/snli/plain_text/1.0.0/1f60b67533b65ae0275561ff7828aad5ee4282d0e6f844fd148d05d3c6ea251b. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"28da8980613145c39b658eb82acba1dd"}},"metadata":{}}],"source":["import pandas as pd\n","dataset = load_dataset(\"snli\")\n","snli_sampled=pd.DataFrame(dataset[\"train\"])\n","snli_sampled=snli_sampled.sample(frac=0.01, random_state=123)"],"id":"GwkbBNtw2Q1D"},{"cell_type":"markdown","source":["# Organize Dataset"],"metadata":{"id":"VZfRyF7GZUbd"},"id":"VZfRyF7GZUbd"},{"cell_type":"code","execution_count":null,"metadata":{"id":"eSgJVYEYrGLY","colab":{"base_uri":"https://localhost:8080/","height":17,"referenced_widgets":["f519c5c611154392b6f14d6a8299a17d","25229b95355242ccbd2d36eb2acea9e8","d8731f9e50ab48368a79e63b899554fa","e9f0e03886c548d68b18197aeae5eadd","e3f5a01f9cdd431681e34e913285f533","accdd0ca97fd42eb9bc98e34a593e490","6d766c889e044d98bcc198b5060801b8","86fcf2921d3c4f1f9cefcc0d21dd8cc3","16aaa2216ddd436393b324469a3fd136","608eccb3bcb8454282e4249e3080197b","b328eaeb16e14386b4785b543ffd20c4","c9a9abd66a43434b91b41215eebe88fd","c18c65c02b864ff8bbe7c5c0b0f6081d","918207b601aa4f62a48b55b6d976324e","3ab2b03f6e34448f8c8729218f4dc86a","e968bf3a1a6a4087bbf373b542921d6c","bfbc38f6b1844d32b7a728ecd6101d53","5e15b3958c014f8eb99153923b639391","e0dacd6c258845fa95db030ca67cb67b","aec7f42f6b7a4b4aad1afa87fd843872","45c21bf6d05f43ffbe2924712fe9e267","e4d4572f207b4d06b43c611585176a15"]},"executionInfo":{"status":"ok","timestamp":1692375492244,"user_tz":-180,"elapsed":820,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"44825c90-5444-44bd-97dd-157171f01870"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/4000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f519c5c611154392b6f14d6a8299a17d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1497 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c9a9abd66a43434b91b41215eebe88fd"}},"metadata":{}}],"source":["from datasets import Dataset\n","\n","mapp={0: \"Entailment: premise entails hypothesis\",\n","      1: \"Neutral: there is no relation between premise and hypothesis\",\n","      2: \"Contradiction: premise contradicts hypothesis\"}\n","\n","snli_sampled= snli_sampled[snli_sampled.label>-1]\n","\n","train_df=snli_sampled[:4000]\n","test_df=snli_sampled[4000:]\n","\n","train_df2=Dataset.from_pandas(train_df)\n","train_df2=train_df2.map(create_prompt_formats_SNLI)\n","\n","test_df2=Dataset.from_pandas(test_df)\n","test_df2=test_df2.map(create_prompt_formats_SNLI)\n","\n","train2.shape, test2.shape"],"id":"eSgJVYEYrGLY"},{"cell_type":"markdown","metadata":{"id":"_nt7kQR_Ic6A"},"source":["# Model Loading"],"id":"_nt7kQR_Ic6A"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":570,"status":"ok","timestamp":1692375220958,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"wgM2KboasnEZ","outputId":"79f7328a-cde7-44f3-9976-826910c7250d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n","Token is valid (permission: read).\n","Your token has been saved to /root/.cache/huggingface/token\n","Login successful\n"]}],"source":["from huggingface_hub import login\n","access_token_read = \"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\"\n","access_token_write = \"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\"\n","login(token = access_token_read)\n","\n","model_name = \"daryl149/llama-2-7b-chat-hf\"\n","bnb_config = create_bnb_config()\n","model, tokenizer = load_model(model_name, bnb_config)"],"id":"wgM2KboasnEZ"},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qoa7K-eHZ-Mn","colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["3ce322af22f14e66b851844261a96996","e87f060ba5794d4796b982c79152111c","b6a08c731f2a424b937c58468e833920","3dddbe60b50343888faaa11e290e4409","e186e6d84bb044c0aa1004c319823e03","ddd9bcb1f9c0487ba7ec2328671863c6","128a4e20fa9b4523a85f5bac5ade016c","94da13da1dac46689090d1b1b28cd88c","d2402dc520424fbc8db8f1c84f77ed3d","cfcdc74b1ea24c46bc75e1e8e909c949","8d76ef52d560473eadfcec714c823b6f"]},"executionInfo":{"status":"ok","timestamp":1692375329528,"user_tz":-180,"elapsed":13215,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"7fe52b7d-36a1-4036-b82f-ed5218386b1d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3ce322af22f14e66b851844261a96996"}},"metadata":{}}],"source":[],"id":"Qoa7K-eHZ-Mn"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":54,"referenced_widgets":["7aceecdc6ed647e39c004eacff358bc1","fcb36bf230904f069b956c0a27a0dd06","fbd64b4924d141e285b842ad8fb1fe3a","600a6c8a4446409a8e532765c19eac29","e56049dece5b49958848db7cb0d0b636","cb9fe1cefd164d5e9874d769d01d2059","0eba5e2650d144228b03911538446fc2","65508873450f471081c1a1f5fbcdb679","5612dac05ab2423caeb2982701941e31","b280c939b56b4d3d8cf3c64c7171575b","2421fb0ef4f644179648be6c808427ee","455aece848da4a7e98b82419bc90a638","b4fefef3034345fca7a91f1a01e59788","ea1aad46ce5a4e1289f9b96f106729c0","e16f5b8ee00f44bf98e73fc7a481841e","ec2394c8cd074da195734bad6e07bb0c","c004111cca3248d3a18985c76e418abb","23f9b735b53a472b8013671c75f2a23a","f01e0c700aaf44dea014526a82a96cec","cdac9769098846a2be5334c9b5fc3446","dbb81bbb732240ae91b3907c59302e8d","3d9d23a605734eed89ba467a689d614a","cd1217b22cb745c39196ce93540c4505","21dbd0dc435644b38a7592d98562762b","ce8adaa486b64827b41ce213dc8dad33","8f19b1bec6784ef0a0719f3737f16154","508eb636346643f194a212abe0dcc7af","14b6a559b2eb42f6b3519f42ecf0396b","8929e68a296e44afa2295253c805b456","5343a23c15e6409da95d230a4ba2f5ff","51906719d1f64dae80cf8dd7acbf120a","fb98715940c54235b6ceacb6d4bc9fe5","5dad99ecc8ec4b229324453ebc79ea1c"]},"executionInfo":{"elapsed":1244,"status":"ok","timestamp":1692375345825,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"9a30l4WnZ-KG","outputId":"24824f1a-7b6f-407e-8821-cb45b9d08def"},"outputs":[{"output_type":"stream","name":"stdout","text":["Found max lenth: 2048\n","Preprocessing dataset...\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/4000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7aceecdc6ed647e39c004eacff358bc1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/4000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"455aece848da4a7e98b82419bc90a638"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Filter:   0%|          | 0/4000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cd1217b22cb745c39196ce93540c4505"}},"metadata":{}}],"source":["## Preprocess dataset\n","max_length = get_max_length(model)\n","trained2_pro = preprocess_dataset(tokenizer, max_length, 42, train2)"],"id":"9a30l4WnZ-KG"},{"cell_type":"code","source":[],"metadata":{"id":"NYCkmB0OcN_u"},"id":"NYCkmB0OcN_u","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Train trainer"],"metadata":{"id":"o6mPSp_5cOdk"},"id":"o6mPSp_5cOdk"},{"cell_type":"code","execution_count":null,"metadata":{"id":"yWEklRows7bY"},"outputs":[],"source":["output_dir = \"results/llama2/final_checkpoint\"\n","train(model, tokenizer, trained2_pro, output_dir)"],"id":"yWEklRows7bY"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":459,"status":"ok","timestamp":1692375872584,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"SsDZcl79s7Ys","outputId":"74f10771-112b-42af-9421-2e322bcd0d43"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'results/llama2/final_checkpoint'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":37}],"source":["output_dir"],"id":"SsDZcl79s7Ys"},{"cell_type":"markdown","source":["# Merging and saving"],"metadata":{"id":"14Rx9HakgKO3"},"id":"14Rx9HakgKO3"},{"cell_type":"code","source":["tokenizer"],"metadata":{"id":"P_zgUneEds05"},"id":"P_zgUneEds05","execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":104,"referenced_widgets":["30ab423f085e4e30ba5d8ae123cd7610","50d8a5da20c4440caaf723067df35657","142f1fff3a0243a3baf4cfd18235f514","2484c81980aa4fe68ef964181ccf28a4","4a99bd1ef8444ba6969f46bdf8e09e4c","d94deee4cb94429ea5e53bda02449153","b43c810d982d484cb43bef694adde1fe","5dea29cc935b4bb1a4fb60d58bb0e4c7","3130bbd48fca48378f87301dec3ecbb1","ea47c8abd97b4031a56f64e7314bbe82","7ec297009792481aaf1fd2152144aad1"]},"executionInfo":{"elapsed":208637,"status":"ok","timestamp":1692376398122,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"euAj7CzSpOXk","outputId":"4aa62b30-66b7-4f56-f1fc-11a84e9ba589"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"30ab423f085e4e30ba5d8ae123cd7610"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["('results/llama2/final_merged_checkpoint/tokenizer_config.json',\n"," 'results/llama2/final_merged_checkpoint/special_tokens_map.json',\n"," 'results/llama2/final_merged_checkpoint/tokenizer.json')"]},"metadata":{},"execution_count":44}],"source":["model = AutoPeftModelForCausalLM.from_pretrained(output_dir, device_map=\"auto\", torch_dtype=torch.bfloat16)\n","model = model.merge_and_unload()\n","output_merged_dir = \"results/llama2/final_merged_checkpoint\"\n","os.makedirs(output_merged_dir, exist_ok=True)\n","model.save_pretrained(output_merged_dir, safe_serialization=True)\n","\n","# save tokenizer for easy inference\n","tokenizer = AutoTokenizer.from_pretrained(model_name)\n","tokenizer.save_pretrained(output_merged_dir)"],"id":"euAj7CzSpOXk"},{"cell_type":"code","source":[],"metadata":{"id":"GXrNF-8Vd_iP"},"id":"GXrNF-8Vd_iP","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Inference and Evaluation"],"metadata":{"id":"YuT-zhwSgNnY"},"id":"YuT-zhwSgNnY"},{"cell_type":"code","execution_count":null,"metadata":{"id":"88JAKzaTpOUx"},"outputs":[],"source":["from transformers import AutoTokenizer\n","import transformers\n","import torch\n","\n","model_path = \"results/llama2/final_merged_checkpoint\"\n","tokenizer = AutoTokenizer.from_pretrained(model_path)\n","\n","pipeline = transformers.pipeline(\n","    \"text-generation\",\n","    model=model_path,\n","    tokenizer=tokenizer,\n","    torch_dtype=torch.float16,\n","    device_map=\"auto\",\n","    #device='cuda:0'\n",")\n","\n","sequences = pipeline(\n","    'I liked \"Breaking Bad\" and \"Band of Brothers\". Do you have any recommendations of other shows I might like?\\n',\n","    do_sample=True,\n","    top_k=10,\n","    num_return_sequences=1,\n","    eos_token_id=tokenizer.eos_token_id,\n","    max_length=200,\n",")\n","for seq in sequences:\n","    print(f\"Result: {seq['generated_text']}\")"],"id":"88JAKzaTpOUx"},{"cell_type":"code","source":["print(test2[\"text\"][0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_ioYe7SNfvpi","executionInfo":{"status":"ok","timestamp":1692376446624,"user_tz":-180,"elapsed":420,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"bf53549c-a45c-44c6-d3d0-60327572fea6"},"id":"_ioYe7SNfvpi","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Below is an NLI instruction that describes a NLI task. .\n","\n","### Instruction:\n"," Detect whether the given “hypothesis” logically follows from the 'premise'\n","\n","Input:\n"," Hypothesis: As it is their first time in the United states,the foreign exchange students are enjoying on the streets of New York., Premise:A group of foreign exchange students having a good time and enjoying the city life in the astonishingly busy streets of New York as they enjoy their first time being in the United States.  \n","\n","### Response:\n","Entailment: premise entails hypothesis\n","\n","### End\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fCJnKO8upOSD"},"outputs":[],"source":["topla=[]\n","for i,t in enumerate(test2['text']):\n","  #print(\"*\"*30)\n","  if i%50==0:\n","    print(i, \"/\",len(test2))\n","  inp= t.split(\"### Response\")[0] +\" ### Response\"\n","  out_real= t.split(\"### Response\")[1]\n","  #print(inp)\n","  #print(\"GROUND:\"+out_real)\n","  sequences = pipeline(\n","    inp,\n","    do_sample=True,\n","    top_k=10,\n","    num_return_sequences=1,\n","    eos_token_id=tokenizer.eos_token_id,\n","    max_length=200,\n","  )\n","  topla.append((inp, out_real,sequences))\n","  #print(\"PRED...\",sequences[0][\"generated_text\"].lower().split(\"response\")[1])"],"id":"fCJnKO8upOSD"},{"cell_type":"code","source":["sequences[0][\"generated_text\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":109},"id":"mBMdQk_fhTbs","executionInfo":{"status":"ok","timestamp":1692376847837,"user_tz":-180,"elapsed":420,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"ab63dbf7-6595-4d76-a87d-0d7223b2ac82"},"id":"mBMdQk_fhTbs","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"Below is an NLI instruction that describes a NLI task. .\\n\\n### Instruction:\\n Detect whether the given “hypothesis” logically follows from the 'premise'\\n\\nInput:\\n Hypothesis: As it is their first time in the United states,the foreign exchange students are enjoying on the streets of New York., Premise:A group of foreign exchange students having a good time and enjoying the city life in the astonishingly busy streets of New York as they enjoy their first time being in the United States.  \\n\\n ### Response:\\nContradiction: premise contradicts hypothesis\\n\\n### End:\\nNeutral: there is no relation between premise and hypothesis\\n\\n### End:\\nNeutral: there is no relation between premise and hypothesis\\n\\n### End:\\nNeutral: there is no relation between premise and hypothesis\\n\\n### End:\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":52}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"i5rBJnihpOPS"},"outputs":[],"source":["pd.DataFrame(topla).to_csv(\"testoutllm.csv\")"],"id":"i5rBJnihpOPS"},{"cell_type":"code","execution_count":null,"metadata":{"id":"sHgny0KZs7V5"},"outputs":[],"source":["q=pd.DataFrame(topla)"],"id":"sHgny0KZs7V5"},{"cell_type":"code","source":["import pandas as pd"],"metadata":{"id":"oN9CJcAmtP3u"},"id":"oN9CJcAmtP3u","execution_count":null,"outputs":[]},{"cell_type":"code","source":["df=pd.read_csv(\"testoutllm.csv\")"],"metadata":{"id":"GCqlceDstWaD"},"id":"GCqlceDstWaD","execution_count":null,"outputs":[]},{"cell_type":"code","source":["df.head(10)"],"metadata":{"id":"7gEi4htvtbgm","executionInfo":{"status":"ok","timestamp":1692380028804,"user_tz":-180,"elapsed":4,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"a19a2ca3-92a1-4874-ff86-8f842e3c5e06","colab":{"base_uri":"https://localhost:8080/","height":1030}},"id":"7gEi4htvtbgm","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Unnamed: 0                                                  0  \\\n","0           0  Below is an NLI instruction that describes a N...   \n","1           1  Below is an NLI instruction that describes a N...   \n","2           2  Below is an NLI instruction that describes a N...   \n","3           3  Below is an NLI instruction that describes a N...   \n","4           4  Below is an NLI instruction that describes a N...   \n","5           5  Below is an NLI instruction that describes a N...   \n","6           6  Below is an NLI instruction that describes a N...   \n","7           7  Below is an NLI instruction that describes a N...   \n","8           8  Below is an NLI instruction that describes a N...   \n","9           9  Below is an NLI instruction that describes a N...   \n","\n","                                                   1  \\\n","0  :\\nEntailment: premise entails hypothesis\\n\\n#...   \n","1  :\\nNeutral: there is no relation between premi...   \n","2  :\\nEntailment: premise entails hypothesis\\n\\n#...   \n","3  :\\nEntailment: premise entails hypothesis\\n\\n#...   \n","4  :\\nEntailment: premise entails hypothesis\\n\\n#...   \n","5  :\\nNeutral: there is no relation between premi...   \n","6  :\\nEntailment: premise entails hypothesis\\n\\n#...   \n","7  :\\nEntailment: premise entails hypothesis\\n\\n#...   \n","8  :\\nEntailment: premise entails hypothesis\\n\\n#...   \n","9  :\\nNeutral: there is no relation between premi...   \n","\n","                                                   2  \n","0  [{'generated_text': \"Below is an NLI instructi...  \n","1  [{'generated_text': \"Below is an NLI instructi...  \n","2  [{'generated_text': \"Below is an NLI instructi...  \n","3  [{'generated_text': \"Below is an NLI instructi...  \n","4  [{'generated_text': \"Below is an NLI instructi...  \n","5  [{'generated_text': \"Below is an NLI instructi...  \n","6  [{'generated_text': \"Below is an NLI instructi...  \n","7  [{'generated_text': \"Below is an NLI instructi...  \n","8  [{'generated_text': \"Below is an NLI instructi...  \n","9  [{'generated_text': \"Below is an NLI instructi...  "],"text/html":["\n","  <div id=\"df-41679c04-243d-4501-99df-fef9ead1b325\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>Below is an NLI instruction that describes a N...</td>\n","      <td>:\\nEntailment: premise entails hypothesis\\n\\n#...</td>\n","      <td>[{'generated_text': \"Below is an NLI instructi...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>Below is an NLI instruction that describes a N...</td>\n","      <td>:\\nNeutral: there is no relation between premi...</td>\n","      <td>[{'generated_text': \"Below is an NLI instructi...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>Below is an NLI instruction that describes a N...</td>\n","      <td>:\\nEntailment: premise entails hypothesis\\n\\n#...</td>\n","      <td>[{'generated_text': \"Below is an NLI instructi...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>Below is an NLI instruction that describes a N...</td>\n","      <td>:\\nEntailment: premise entails hypothesis\\n\\n#...</td>\n","      <td>[{'generated_text': \"Below is an NLI instructi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>Below is an NLI instruction that describes a N...</td>\n","      <td>:\\nEntailment: premise entails hypothesis\\n\\n#...</td>\n","      <td>[{'generated_text': \"Below is an NLI instructi...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>Below is an NLI instruction that describes a N...</td>\n","      <td>:\\nNeutral: there is no relation between premi...</td>\n","      <td>[{'generated_text': \"Below is an NLI instructi...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>Below is an NLI instruction that describes a N...</td>\n","      <td>:\\nEntailment: premise entails hypothesis\\n\\n#...</td>\n","      <td>[{'generated_text': \"Below is an NLI instructi...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>7</td>\n","      <td>Below is an NLI instruction that describes a N...</td>\n","      <td>:\\nEntailment: premise entails hypothesis\\n\\n#...</td>\n","      <td>[{'generated_text': \"Below is an NLI instructi...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>8</td>\n","      <td>Below is an NLI instruction that describes a N...</td>\n","      <td>:\\nEntailment: premise entails hypothesis\\n\\n#...</td>\n","      <td>[{'generated_text': \"Below is an NLI instructi...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>9</td>\n","      <td>Below is an NLI instruction that describes a N...</td>\n","      <td>:\\nNeutral: there is no relation between premi...</td>\n","      <td>[{'generated_text': \"Below is an NLI instructi...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41679c04-243d-4501-99df-fef9ead1b325')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path 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a.lower():\n","    return \"Contradiction\"\n","   if \"entailment\" in a.lower():\n","    return \"Entailment\"\n","   return \"\"\n"],"id":"tvI99AsrPwX8"},{"cell_type":"code","execution_count":null,"metadata":{"id":"oUlYOlq6RS0y"},"outputs":[],"source":[],"id":"oUlYOlq6RS0y"},{"cell_type":"code","execution_count":null,"metadata":{"id":"pJFJF79tRjQv"},"outputs":[],"source":[],"id":"pJFJF79tRjQv"},{"cell_type":"code","execution_count":null,"metadata":{"id":"yjeWBoWbP3Od"},"outputs":[],"source":["q[\"pred2\"]=q.pred.apply(lambda x: extract_answer(x[0]['generated_text']))"],"id":"yjeWBoWbP3Od"},{"cell_type":"code","execution_count":null,"metadata":{"id":"X-JoCZOPRDy6"},"outputs":[],"source":["q[\"real2\"]=q.real.apply(lambda x: extract_real(x))"],"id":"X-JoCZOPRDy6"},{"cell_type":"code","execution_count":null,"metadata":{"id":"dosMRXseRJm9"},"outputs":[],"source":["result= list(zip(q.real2, q.pred2))"],"id":"dosMRXseRJm9"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1692305728093,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"P0ge-3VSRJkl","outputId":"7ffaf04e-1be6-4b28-95f6-5bac94ece207"},"outputs":[{"data":{"text/plain":["(426, 1497, 0.2845691382765531)"]},"execution_count":79,"metadata":{},"output_type":"execute_result"}],"source":["res2=[a==b for a,b in result]\n","sum(res2), len(res2), sum(res2) / len(res2),"],"id":"P0ge-3VSRJkl"},{"cell_type":"code","execution_count":null,"metadata":{"id":"kq9ZRKFuRJh1"},"outputs":[],"source":["result"],"id":"kq9ZRKFuRJh1"},{"cell_type":"code","execution_count":null,"metadata":{"id":"tPDU5P61RJdo"},"outputs":[],"source":[],"id":"tPDU5P61RJdo"},{"cell_type":"markdown","metadata":{"id":"SMVIn-0itEsi"},"source":["# diger bazı kodlar"],"id":"SMVIn-0itEsi"},{"cell_type":"code","execution_count":null,"metadata":{"id":"onTs33D8r8x2"},"outputs":[],"source":["#%%capture\n","!pip install transformers==4.29.0  peft datasets==2.12.0"],"id":"onTs33D8r8x2"},{"cell_type":"code","execution_count":null,"metadata":{"id":"FQo2BljWk_Ll"},"outputs":[],"source":["#pip install peft"],"id":"FQo2BljWk_Ll"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2467,"status":"ok","timestamp":1692033881560,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"nXFwrtm_TABK","outputId":"793a17e8-1a9a-4d3f-8746-c7c9f5873ea8"},"outputs":[{"name":"stdout","output_type":"stream","text":["transformers                     4.29.0\n","peft                             0.4.0\n","datasets                         2.12.0\n"]}],"source":["!pip list |egrep transformers\n","!pip list |egrep peft\n","!pip list |egrep ^datasets\n"],"id":"nXFwrtm_TABK"},{"cell_type":"code","execution_count":null,"metadata":{"id":"5f93b7d1"},"outputs":[],"source":["from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n","from transformers import (default_data_collator,\n","                          get_linear_schedule_with_warmup)\n","from peft import (get_peft_config,\n","                  get_peft_model,\n","                  get_peft_model_state_dict,\n","                  LoraConfig,\n","                  TaskType)\n","import torch, os\n","from torch.utils.data import DataLoader\n","from tqdm import tqdm\n","import pandas as pd\n","from datasets import (load_dataset,\n","                      Dataset, DatasetDict)"],"id":"5f93b7d1"},{"cell_type":"markdown","metadata":{"id":"lZCKJEpMMURo"},"source":["# Loading SNLI dataset from huggingface dataset hub"],"id":"lZCKJEpMMURo"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":329,"referenced_widgets":["543b416dbf26401eb89cf183453cb341","c73d2402527c4b33b55fe8bdfea2669a","efb8be5f83b34cde80e33d93e5d204ca","bfabc41e6e0e412b9143c969d5155847","a0939d890cff48c7a67f8f19f1d1c6f6","ee6328c214874795a7dbed5aa370d8ac","62699c7c19734f7cb894cd223dabbc38","83cfdebe3a024974a8ed511f1315a9ec","62186739888a4372aca1ece4f4de59a5","e5abbe176ddf4db6b31fb674d2bd4400","d42546d4a7714b8583f481a30919aaa3","df4dab0047414cd58c727a5681609da4","c4ecdb470a7247bd8a64026e3092fca9","b3994724339e444fa00c90a2a8329c1b","250d142080f24d9c8435ce66025d4b2f","ba392e4034b64d83a08d5fc1d86c8c11","b1c175419fe842428ae3cd28ed6b055f","8f500ba15e9040dba0591379c8a07b81","8b82b0ce17464beeb952f11b120b6093","13c9d34357e24ffead7d616a7d7025a1","f9a02303801447e0913b4bba73519c31","a92c163d61b6448e9a8c73217de3bfcd","0c17749ad0bd41289c5b900da553cfc8","5dfdac46ff3246eda8374656f7897e17","276eba4b353a4fde891e9719f86fa792","c79088f8f40e460abe45b6b12dc97b20","cb9baad87be64307aa6293b7bf9136a4","298270f0d64b4b53a745d39bfe94ebac","e62d748c36104e26a5d13dca9dd1072c","a1d932b547b844a397b5ca2089ef9755","716763955aa646e29aeac0298b402fda","fdfea3ec51b846869b409aa09fd15c4e","238d3da28a5141d09b2cbc0f7c308620","89f56ba09e6c4d27906f979618f7b18d","82b7877dd2a94a89be0a46d9b54ca52b","1b3c2ec716e54856aef83cef2c54733d","9e155200140a414884325c6094f0a535","9d8b1b7f11cf455a9345d6036083039b","076a5938df2b4e89949868d8a63b9b72","63f9ef6509194484b7b682de46c3bae1","2a57ce0a8a0440c4b7c0a3613289fbb2","d7dad62bc5694083a3a3eb540e9bd2d6","afa103188d77426d913cd43ae67312c2","13a46eaa6d274f59bf06ee0fcc87700c","9191a67402d54b73a7ae0dc4ec0e51d4","efa350b6452842668bb3ad48eca53a60","6d2b85ada6284d77960b80ba2598c0af","95d9472b44834f4e8f032c73fa85d4a5","379b984a58cb49a598398d7286fa59bd","2678a2751f1444ffa0cfbc39b82aeadc","0ffcbd20885d42b2ad2318ed124827db","925d526cba9f43b8bd81c71bb5d7ec47","817b9d60985b48b3ac1a1d625dce800a","b8d92876d659482ca007c98d4d287855","695919e60f2441d5b51d810f8aa1f5c2","da4da90b80f04872ade6dbf0a800f36f","fa0e9438b2684d98bcb167dadada5718","4b922cd4d8e4498986b950c3c9df02d3","4cf952df893b43eb97cbea1c1890969a","cd4c6bcf7b5c4cafb0d503e482247d6d","2874843af7544b178b274fdab87ee2fd","8e1bd2fd5c33413a981d9697bd2a7850","67b5ad49efa1475496767124f39e965b","6a33bcbc7b714aa3b20c7e7ad754b768","2e2d38060db74655bec7ac1ffa07d3ed","5e0c73ef3885432b98544458bbbfaf6a","a9def35f9fda4ce5b5f7a4ccc5276866","6832ca7817694d369f44ec1f77fc73f2","636876fe30194b33b714ac124986127e","3f8e4adfb5964cb6babeb1a6597ece50","cc0440e9d7624e98a2390c7abba15b89","17744316f5f0431795df94bcbd229360","6ceaaabcd1b9433082319296c6057bfb","986c76cc7f47451482cfe9da4066b957","7554d5c15e494f598c40f392cb86fd0c","d3309ef4ee9f498c87e56bc54fd51f64","c260884906eb4e08829a9f9536f8f159","7dd3c0eec5c948b9b548687821af4d1c","79f695ca42284335942a60aec0fae6d6","464568b4648541cf92bfb272744c27a4","326ce78b87b542c1a497cf72522e926b","932a8319b60648b0944900868f9032eb","898c5ce684e14c3894cd3b521bddcfc8","542e1fd8c5034ad99aa96abad585dd60","1c3cc8c20f5d4adf8d51c2d4caee010d","3978644559194406b60cbc191f3e1450","10f6ac2ce3d94a59a75d3a121935dbe0","c0b2d040d68d45da955ec480cc0919df"]},"executionInfo":{"elapsed":37584,"status":"ok","timestamp":1692033926547,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"GLCU6GVN-pBC","outputId":"035ada9e-4763-401d-d820-b3aa18b288cf"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"543b416dbf26401eb89cf183453cb341","version_major":2,"version_minor":0},"text/plain":["Downloading builder script:   0%|          | 0.00/3.82k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"df4dab0047414cd58c727a5681609da4","version_major":2,"version_minor":0},"text/plain":["Downloading metadata:   0%|          | 0.00/1.90k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0c17749ad0bd41289c5b900da553cfc8","version_major":2,"version_minor":0},"text/plain":["Downloading readme:   0%|          | 0.00/14.1k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Downloading and preparing dataset snli/plain_text to /root/.cache/huggingface/datasets/snli/plain_text/1.0.0/1f60b67533b65ae0275561ff7828aad5ee4282d0e6f844fd148d05d3c6ea251b...\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"89f56ba09e6c4d27906f979618f7b18d","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          | 0.00/1.93k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9191a67402d54b73a7ae0dc4ec0e51d4","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          | 0.00/1.26M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"da4da90b80f04872ade6dbf0a800f36f","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          | 0.00/65.9M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a9def35f9fda4ce5b5f7a4ccc5276866","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          | 0.00/1.26M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Dataset snli downloaded and prepared to /root/.cache/huggingface/datasets/snli/plain_text/1.0.0/1f60b67533b65ae0275561ff7828aad5ee4282d0e6f844fd148d05d3c6ea251b. Subsequent calls will reuse this data.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7dd3c0eec5c948b9b548687821af4d1c","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"}],"source":["dataset = load_dataset(\"snli\")\n","snli_sampled=pd.DataFrame(dataset[\"train\"])\n","snli_sampled=snli_sampled.sample(frac=0.01, random_state=123)"],"id":"GLCU6GVN-pBC"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":45,"status":"ok","timestamp":1692033926548,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"rJfgeX6z_Nw1","outputId":"a2838277-b563-49cb-bfff-d29fe7e348c8"},"outputs":[{"data":{"text/plain":["(5502, 3)"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["snli_sampled.shape"],"id":"rJfgeX6z_Nw1"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":39,"status":"ok","timestamp":1692033926549,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"4A8do1hj-mcs","outputId":"823ca147-1ddc-432d-99d9-5618e5bf1693"},"outputs":[{"data":{"text/plain":[" 2    1866\n"," 0    1828\n"," 1    1803\n","-1       5\n","Name: label, dtype: int64"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["snli_sampled.label.value_counts()"],"id":"4A8do1hj-mcs"},{"cell_type":"code","execution_count":null,"metadata":{"id":"laoIiiLp_TBk"},"outputs":[],"source":["snli_sampled= snli_sampled[snli_sampled.label>-1]"],"id":"laoIiiLp_TBk"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":31,"status":"ok","timestamp":1692033926550,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"0DKTCDX6_S_B","outputId":"d8c47404-f612-4686-d754-be78eb972493"},"outputs":[{"data":{"text/plain":["['entailment', 'neutral', 'contradiction']"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["names=dataset[\"train\"].features[\"label\"].names\n","names"],"id":"0DKTCDX6_S_B"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26,"status":"ok","timestamp":1692033926550,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"UDEVPadF_S8Z","outputId":"3a3200a5-219b-44bb-c1cb-93d698f5819d"},"outputs":[{"data":{"text/plain":["{0: 'entailment', 1: 'neutral', 2: 'contradiction'}"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["mapp=dict(enumerate(names))\n","mapp"],"id":"UDEVPadF_S8Z"},{"cell_type":"markdown","metadata":{"id":"SwXwnhgCOF_m"},"source":["## Preparing dataset for training"],"id":"SwXwnhgCOF_m"},{"cell_type":"code","execution_count":null,"metadata":{"id":"dellBc6o_S58"},"outputs":[],"source":["snli_sampled_df= pd.DataFrame(snli_sampled)\n","snli_sampled_df[\"text\"]= snli_sampled_df\\\n","      .apply(lambda x: \"S1:\" +x.premise\n","             +\" S2:\"+x.hypothesis+\n","             \". The relation between S1 and S2 is labeled \"+\n","             \"as entailment, neutral or contradiction ?\",\n","            axis=1)\n","snli_sampled_df[\"label\"]=snli_sampled_df\\\n","        .apply(lambda x: f\"It is {mapp[x.label]}\",\n","               axis=1)"],"id":"dellBc6o_S58"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1692033926551,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"YiYgx9CAFMCj","outputId":"7bfc3dee-3c74-4928-b86f-b07270e260fd"},"outputs":[{"data":{"text/html":["\n","\n","  <div id=\"df-15439d2d-9d1d-40ad-82e0-9aa7e5450656\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>premise</th>\n","      <th>hypothesis</th>\n","      <th>label</th>\n","      <th>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>190268</th>\n","      <td>Two firefighters clad in protective gear are e...</td>\n","      <td>Two firefighters are entering a house.</td>\n","      <td>It is entailment</td>\n","      <td>S1:Two firefighters clad in protective gear ar...</td>\n","    </tr>\n","    <tr>\n","      <th>300525</th>\n","      <td>Two men work together on a construction project.</td>\n","      <td>Two men are working.</td>\n","      <td>It is entailment</td>\n","      <td>S1:Two men work together on a construction pro...</td>\n","    </tr>\n","    <tr>\n","      <th>465851</th>\n","      <td>Three men in uniform walk around town.</td>\n","      <td>Three men rob the residents.</td>\n","      <td>It is contradiction</td>\n","      <td>S1:Three men in uniform walk around town. S2:T...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-15439d2d-9d1d-40ad-82e0-9aa7e5450656')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","\n","\n","\n","    <div id=\"df-b5813d8f-a7b9-46aa-9ce3-c8c1075ce15e\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b5813d8f-a7b9-46aa-9ce3-c8c1075ce15e')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","    <script>\n","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-b5813d8f-a7b9-46aa-9ce3-c8c1075ce15e button.colab-df-quickchart');\n","  quickchartButtonEl.style.display =\n","    google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-15439d2d-9d1d-40ad-82e0-9aa7e5450656');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"],"text/plain":["                                                  premise  \\\n","190268  Two firefighters clad in protective gear are e...   \n","300525   Two men work together on a construction project.   \n","465851             Three men in uniform walk around town.   \n","\n","                                    hypothesis                label  \\\n","190268  Two firefighters are entering a house.     It is entailment   \n","300525                    Two men are working.     It is entailment   \n","465851            Three men rob the residents.  It is contradiction   \n","\n","                                                     text  \n","190268  S1:Two firefighters clad in protective gear ar...  \n","300525  S1:Two men work together on a construction pro...  \n","465851  S1:Three men in uniform walk around town. S2:T...  "]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["snli_sampled_df.head(3)"],"id":"YiYgx9CAFMCj"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1692033926553,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"eLON7CJh_S3S","outputId":"c2fe6421-0267-4234-fcd2-0d9acbfb4e45"},"outputs":[{"name":"stdout","output_type":"stream","text":["Training set size is 3847\n","Validation set size is         1650\n","Total size is 5497\n"]}],"source":["CUT=snli_sampled_df.shape[0]*7//10\n","print(f\"Training set size is {CUT}\")\n","print(f\"Validation set size is \\\n","        {snli_sampled_df.shape[0]-CUT}\")\n","print(f\"Total size is {snli_sampled_df.shape[0]}\")\n","\n","snli_sampled_dict= DatasetDict({\"train\":\n","                         Dataset.from_pandas(snli_sampled_df[:CUT]),\n","                        \"validation\":\n","                         Dataset.from_pandas(snli_sampled_df[CUT:]),\n","                        })"],"id":"eLON7CJh_S3S"},{"cell_type":"code","execution_count":null,"metadata":{"id":"wpHG6yhdAXDW"},"outputs":[],"source":["def preprocess_function(examples):\n","    inputs = examples[\"text\"]\n","    targets = examples[\"label\"]\n","    model_inputs = tokenizer(inputs, max_length=max_length,\n","                             padding=\"max_length\",\n","                             truncation=True,\n","                             return_tensors=\"pt\")\n","    labels = tokenizer(targets,\n","                       max_length=max_target_len,\n","                       padding=\"max_length\",\n","                       truncation=True,\n","                       return_tensors=\"pt\")\n","    labels = labels[\"input_ids\"]\n","    labels[labels == tokenizer.pad_token_id] = -100\n","    model_inputs[\"labels\"] = labels\n","    return model_inputs"],"id":"wpHG6yhdAXDW"},{"cell_type":"code","execution_count":null,"metadata":{"id":"qNcLBqxAk0RX"},"outputs":[],"source":[],"id":"qNcLBqxAk0RX"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["694f1d7519684f668eaa0129b4f62f9e","b81b69994fd04f1e9bd1c444d4d43ed2","680c67acd66a4c3facef947bd6e3aabb","d38a1068cefc44a9a501da6dcc091d60","7e8443c4a57b4212aa8f9a29a251241c","7ce1d18b8c0b482698c73829c64c6623","2b619b03dd2041debe6b6182a1250471","72f6e13558b04c2cb323a91a97cbbe52","689099aa889843659e06dd19d157f449","6a0d97f799b341f28109fcb96788d2a5","cd2fa29adf854997ac967cb9220e0f9f","6563800b286845f784d72e511921dae6","5a31a654386a45ad9ec900ae47398aca","b475f2bf1f404272a64a12762f0bced8","50ce76c832a14f529ab6f069ecad580b","83e308dc7b8442a097d3e9f2ec681ee0","d238bba8ef654847a757ed8171b9c515","e2ab7de9adae4622aa71aef21de6fc4e","60a38098e46d4693a1c0d4af3b6b54e7","cb0168b6b11f48a2b91f92f89cb55bb1","2f689ea474894581a3aeb7938f5074b5","f8c0b434d9f14577bd31459009d27108","bdc63e2b9e6f4acb9b70cc8a07bbdbe3","eb3b67324c0a4b3292a1b5bb0ef1da8e","601617e091ce4b2eaaa4dc4f052bc39b","11261f9d592b4961bf2808e41da5168e","81d062fcb6614e8bb785fd844104cc4c","5c6d4b888c564e1289dba77e7af61352","cbf5a423c93e4ca9b88451c5ea591b23","cba2d4883dcc4547941fcdde3ae7f54b","ddbe04d91b9b4b72ae9112d1d1fcb3f5","2811b08c32eb46bcaae8abb9329cd142","1e334ec0bc314aa8937ce4feee04cd30","c44984e8a8b1426abc9ed886ca7a3908","ffa1d27277614a5891da2ce2276349b8","ce886d08576f40a7b3adb6892da0c7a0","261ef2b5dbb542868d0c259a3998f9d5","08f58a4a75ad43e99fce648f453a6f5e","cad37ff010cc46d680d4b4426f9957b4","d7c82dd43f3d414b805709faa772f526","aa6163b1c06348dfa95d39c55cfc0cac","811d2355a8af4173823acd5de4f498cc","cf9a99608fb5401c813fe4fca38f8e76","b383dced1adf4c8593e75872707e391a"]},"executionInfo":{"elapsed":3229,"status":"ok","timestamp":1692033929766,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"hFdbH42kkige","outputId":"28930099-3389-4397-d693-28e1068e6cf1"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"694f1d7519684f668eaa0129b4f62f9e","version_major":2,"version_minor":0},"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6563800b286845f784d72e511921dae6","version_major":2,"version_minor":0},"text/plain":["Downloading spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"bdc63e2b9e6f4acb9b70cc8a07bbdbe3","version_major":2,"version_minor":0},"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c44984e8a8b1426abc9ed886ca7a3908","version_major":2,"version_minor":0},"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["model_name_or_path=\"google/flan-t5-base\" # 250M parameters\n","#model_name_or_path=\"google/flan-t5-large\" # 780M parameters\n","#model_name_or_path=\"google/flan-t5-xl\" # 3B parameters\n","tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)"],"id":"hFdbH42kkige"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17,"referenced_widgets":["89514176d5cf4b74a87f65f703d515a9","296a5049957c43e4a3c6338ffdf527e0","d51aabe1b5324b8aa62fe6f9bc0811e5","e273f5786c204d9398a5df154830d85b","aa6ef2f9d2694d0891bb35603abe05a2","88dfb1de610949b1aca2e137b16821a4","aae8ef88620d46088ad3ccde636e9cf2","56c97096c9f14aad9c3ac1e0aad0afdb","fb2c6f26b1574b419570e819aa323a5c","314f609fac164b0e927c8113e9084bde","3218ddbd02c44dac8ad7d4c9cc3d4a01","03979891ba66453c9a415f25b1179e6b","fe471cc2733b46adb2b805e132369bfc","781c8885773a49d38e891001c7c23b59","473332e6cbe448e0b2a399cf139bc950","1dc5f1a40de64c11996362b41e5946ce","518ef91e02c34a2191830199e1f2f69a","20ffa0dd90fc4a6f940c90535aab5492","c7bb64f3aef5449e904634cde71585e1","651f171eb1e64dc59e074b5fdf2558c3","aa8ec48d010f4df78ddd7c2bab3c6a8d","5bc127df949c47f3a6a65d2e3e37c03a"]},"executionInfo":{"elapsed":1386,"status":"ok","timestamp":1692033931112,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"DXpKZLRB_Svs","outputId":"623041ff-f8d6-4a4f-a2e2-7935a7479037"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"89514176d5cf4b74a87f65f703d515a9","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/3847 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"03979891ba66453c9a415f25b1179e6b","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/1650 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"}],"source":["max_length = 150\n","max_target_len=10\n","\n","snli_processed = snli_sampled_dict.map(\n","    preprocess_function,\n","    batched=True,\n","    num_proc=1,\n","    remove_columns=snli_sampled_dict[\"train\"].column_names,\n","    load_from_cache_file=False,\n","  )\n","train_dataset = snli_processed[\"train\"]\n","eval_dataset = snli_processed[\"validation\"]"],"id":"DXpKZLRB_Svs"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"elapsed":437,"status":"ok","timestamp":1692033931546,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"5IbOwEYmGdvv","outputId":"3e3a079e-0397-40f9-e6bd-deb34a1b4300"},"outputs":[{"data":{"text/html":["\n","\n","  <div id=\"df-3e2c43b0-ff3f-45aa-94c9-f03861562235\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>input_ids</th>\n","      <th>attention_mask</th>\n","      <th>labels</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[180, 536, 10, 382, 210, 32, 29764, 3, 4651, 2...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n","      <td>[94, 19, 3, 35, 5756, 297, 1, -100, -100, -100]</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>[180, 536, 10, 382, 210, 32, 1076, 161, 544, 3...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n","      <td>[94, 19, 3, 35, 5756, 297, 1, -100, -100, -100]</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>[180, 536, 10, 11889, 15, 15, 1076, 16, 7117, ...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n","      <td>[94, 19, 27252, 1, -100, -100, -100, -100, -10...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-3e2c43b0-ff3f-45aa-94c9-f03861562235');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["pd.DataFrame(train_dataset).head(3)"],"id":"5IbOwEYmGdvv"},{"cell_type":"code","execution_count":null,"metadata":{"id":"FKmTJORu-lcJ"},"outputs":[],"source":["batch_size = 32\n","train_dataloader = DataLoader(\n","    train_dataset,\n","    shuffle=True,\n","    collate_fn=default_data_collator,\n","    batch_size=batch_size,\n","    pin_memory=True\n",")\n","eval_dataloader = DataLoader(\n","    eval_dataset,\n","    collate_fn=default_data_collator,\n","    batch_size=batch_size,\n","    pin_memory=True)"],"id":"FKmTJORu-lcJ"},{"cell_type":"markdown","metadata":{"id":"QNM5kx3_OPme"},"source":["# Initilizing and Training PEFT model with LORA"],"id":"QNM5kx3_OPme"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3514,"status":"ok","timestamp":1692034363148,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"UUiNdc6LwwxV","outputId":"2718dda8-8401-4731-ab10-e3370d1a9bea"},"outputs":[{"name":"stdout","output_type":"stream","text":["trainable params: 884,736 || all params: 248,462,592 || trainable%: 0.3560841867092814\n"]}],"source":["# creating model with peft\n","# if you want to train the pipline in vanilla mode, set with_peft=False\n","with_peft=True\n","model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n","lr=2e-5\n","if with_peft:\n","  lr=1e-3\n","  peft_config = LoraConfig(task_type=TaskType.SEQ_2_SEQ_LM,\n","                           inference_mode=False,\n","                           r=8,\n","                           lora_alpha=32,\n","                           lora_dropout=0.1)\n","  model = get_peft_model(model, peft_config)\n","  model.print_trainable_parameters()"],"id":"UUiNdc6LwwxV"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":396,"status":"ok","timestamp":1692034383630,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"RmpXL5lPG7ag","outputId":"d0b05e1a-1a07-4084-e4c0-5ffabb83d6b7"},"outputs":[{"data":{"text/plain":["[]"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":[],"id":"RmpXL5lPG7ag"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":371897,"status":"ok","timestamp":1692026111326,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"6b3a4090","outputId":"c20e59dd-51d1-485e-e727-8a8ee23a28a3"},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 121/121 [01:41<00:00,  1.19it/s]\n","100%|██████████| 52/52 [00:20<00:00,  2.48it/s]\n"]},{"name":"stdout","output_type":"stream","text":["epoch=0-> train_loss_avg=tensor(0.3133, device='cuda:0')\t eval_loss_avg=tensor(0.1024, device='cuda:0')\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 121/121 [01:40<00:00,  1.20it/s]\n","100%|██████████| 52/52 [00:20<00:00,  2.49it/s]\n"]},{"name":"stdout","output_type":"stream","text":["epoch=1-> train_loss_avg=tensor(0.1119, device='cuda:0')\t eval_loss_avg=tensor(0.0852, device='cuda:0')\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 121/121 [01:41<00:00,  1.20it/s]\n","100%|██████████| 52/52 [00:20<00:00,  2.49it/s]"]},{"name":"stdout","output_type":"stream","text":["epoch=2-> train_loss_avg=tensor(0.0946, device='cuda:0')\t eval_loss_avg=tensor(0.0861, device='cuda:0')\n"]},{"name":"stderr","output_type":"stream","text":["\n"]}],"source":["device=\"cuda\"\n","model = model.to(device)\n","num_epochs = 3\n","\n","# optimizer and lr scheduler\n","optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n","lr_scheduler = get_linear_schedule_with_warmup(\n","    optimizer=optimizer,\n","    num_warmup_steps=0,\n","    num_training_steps=(len(train_dataloader) * num_epochs),\n",")\n","\n","import time\n","st = time.time()\n","for epoch in range(num_epochs):\n","    model.train()\n","    total_loss = 0\n","    for step, batch in enumerate(tqdm(train_dataloader)):\n","        batch = {k: v.to(device) for k, v in batch.items()}\n","        outputs = model(**batch)\n","        loss = outputs.loss\n","        total_loss += loss.detach().float()\n","        loss.backward()\n","        optimizer.step()\n","        lr_scheduler.step()\n","        optimizer.zero_grad()\n","\n","    model.eval()\n","    eval_loss = 0\n","    eval_preds = []\n","    for step, batch in enumerate(tqdm(eval_dataloader)):\n","        batch = {k: v.to(device) for k, v in batch.items()}\n","        with torch.no_grad():\n","            outputs = model(**batch)\n","        loss = outputs.loss\n","        eval_loss += loss.detach().float()\n","        eval_preds.extend(\n","            tokenizer.batch_decode(\n","                torch.argmax(outputs.logits, -1)\\\n","                .detach().cpu().numpy(),\n","                skip_special_tokens=True)\n","        )\n","    eval_loss_avg = eval_loss / len(eval_dataloader)\n","    train_loss_avg = total_loss / len(train_dataloader)\n","    print(f\"{epoch=}-> {train_loss_avg=}\\t {eval_loss_avg=}\")\n","et = time.time()\n","elapsed_time = et - st"],"id":"6b3a4090"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1692026111327,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"fSjt3zxlbZ01","outputId":"280d1d89-0005-4755-aec8-0c010a370984"},"outputs":[{"name":"stdout","output_type":"stream","text":["model_name_or_path='google/flan-t5-base'\n","num_epochs=3\n","elapsed_time=366.41 seconds with PEFT\n","Accuracy:0.87\n"]}],"source":["zipped=zip(eval_preds, snli_sampled_dict[\"validation\"][\"label\"])\n","q=[real.strip() in pred.strip() for pred,real in zipped]\n","print(f\"{model_name_or_path=}\")\n","print(f\"{num_epochs=}\")\n","print(f\"{elapsed_time=:.2f} seconds\"\n","     + (\" with PEFT\" if with_peft else  \" without PEFT\"))\n","print(f\"Accuracy:{sum(q)/len(q):.2f}\")"],"id":"fSjt3zxlbZ01"},{"cell_type":"markdown","metadata":{"id":"FkeK06D3QbtW"},"source":[],"id":"FkeK06D3QbtW"},{"cell_type":"code","execution_count":null,"metadata":{"id":"Fnsz6AVImBDj"},"outputs":[],"source":[],"id":"Fnsz6AVImBDj"},{"cell_type":"markdown","metadata":{"id":"wMiysPBaJx9Q"},"source":["# Saving PEFT models"],"id":"wMiysPBaJx9Q"},{"cell_type":"code","execution_count":null,"metadata":{"id":"a8de6005"},"outputs":[],"source":["# saving model\n","peft_model_path=\"my_lora_model\"\n","model.save_pretrained(peft_model_path)"],"id":"a8de6005"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1685267344511,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"wmYS-0zAPDIZ","outputId":"d298cb76-0fb4-4af4-ded3-3d5f9eafc403"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 3.5M\n","-rw-r--r-- 1 root root  332 May 28 09:49 adapter_config.json\n","-rw-r--r-- 1 root root 3.5M May 28 09:49 adapter_model.bin\n"]}],"source":["!ls -lh $peft_model_path"],"id":"wmYS-0zAPDIZ"},{"cell_type":"markdown","metadata":{"id":"n2cS0ZKWGfVB"},"source":["# Şuna Bir bakalimü\n","\n","merge filan"],"id":"n2cS0ZKWGfVB"},{"cell_type":"code","execution_count":null,"metadata":{"id":"MkBiJyfDGe1t"},"outputs":[],"source":["model = AutoPeftModelForCausalLM.from_pretrained(output_dir, device_map=\"auto\", torch_dtype=torch.bfloat16)\n","model = model.merge_and_unload()\n","\n","output_merged_dir = \"results/llama2/final_merged_checkpoint\"\n","os.makedirs(output_merged_dir, exist_ok=True)\n","model.save_pretrained(output_merged_dir, safe_serialization=True)\n","\n","# save tokenizer for easy inference\n","tokenizer = AutoTokenizer.from_pretrained(model_name)\n","tokenizer.save_pretrained(output_merged_dir)"],"id":"MkBiJyfDGe1t"},{"cell_type":"markdown","metadata":{"id":"NtC15V8ER5wb"},"source":["# Loading Saved PEFT model"],"id":"NtC15V8ER5wb"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":513,"status":"ok","timestamp":1685267379389,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"76c2fc29","outputId":"436c2383-a49f-4089-e9e2-5496d52d12bd"},"outputs":[{"data":{"text/plain":["PeftConfig(peft_type='LORA', base_model_name_or_path='google/flan-t5-base', task_type='SEQ_2_SEQ_LM', inference_mode=True)"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["from peft import PeftModel, PeftConfig\n","config = PeftConfig.from_pretrained(peft_model_path)\n","config"],"id":"76c2fc29"},{"cell_type":"code","execution_count":null,"metadata":{"id":"0mWGEshUQ_4y"},"outputs":[],"source":["model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n","model = PeftModel.from_pretrained(model, peft_model_path)\n","model.eval()"],"id":"0mWGEshUQ_4y"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1685267404981,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"kqizWfRTRVx7","outputId":"607dcecb-e17b-441c-a3f2-2140c2bb42b1"},"outputs":[{"name":"stdout","output_type":"stream","text":["my_text='S1:The young man wearing a blue sweatshirt and blue jeans is hopping over the railing on the fence. S2:The young man is hopping over the railing to save the young girl from drowning in the pool.. The relation between S1 and S2 is labeled as entailment, neutral or contradiction ?'\n","my_label='It is neutral'\n"]}],"source":["my_text= snli_sampled_dict[\"validation\"][\"text\"][0]\n","my_label= snli_sampled_dict[\"validation\"][\"label\"][0]\n","print(f\"{my_text=}\")\n","print(f\"{my_label=}\")"],"id":"kqizWfRTRVx7"},{"cell_type":"code","execution_count":null,"metadata":{"id":"Fx7RxxozSU0R"},"outputs":[],"source":[],"id":"Fx7RxxozSU0R"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":507,"status":"ok","timestamp":1685267449616,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"37d712ce","outputId":"769a77de-6950-4313-cd69-50ec188000ea"},"outputs":[{"name":"stdout","output_type":"stream","text":["{'input_ids': tensor([[  180,   536,    10,   634,  1021,   388,  5119,     3,     9,  1692,\n","         10242,  9486,    11,  1692, 14118,    19,     3, 21714,   147,     8,\n","          6579,    53,    30,     8,  8227,     5,   180,   357,    10,   634,\n","          1021,   388,    19,     3, 21714,   147,     8,  6579,    53,    12,\n","          1097,     8,  1021,  3202,    45, 24614,    53,    16,     8,  2201,\n","             5,     5,    37,  4689,   344,   180,   536,    11,   180,   357,\n","            19,  3783,    15,    26,    38,     3,    35,  5756,   297,     6,\n","          7163,    42, 27252,     3,    58,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","         1, 1, 1, 1]])}\n"]}],"source":["inputs = tokenizer(my_text, return_tensors=\"pt\")\n","print(inputs)"],"id":"37d712ce"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":596,"status":"ok","timestamp":1685046175837,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"8HdajMpARJqU","outputId":"bae6430e-f47c-46f8-d519-4d168075524b"},"outputs":[{"name":"stdout","output_type":"stream","text":["tensor([[   0,   94,   19, 7163,    1]])\n","['It is neutral']\n"]}],"source":["with torch.no_grad():\n","    outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n","    print(outputs)\n","    print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"],"id":"8HdajMpARJqU"},{"cell_type":"markdown","metadata":{"id":"Sln5kRJeucy8"},"source":["#Train with QLora"],"id":"Sln5kRJeucy8"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8504,"status":"ok","timestamp":1692029280771,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"gxcYXS_pu0xq","outputId":"55d042cf-d2b8-43e2-c091-d7e2c88391f5"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting bitsandbytes\n","  Downloading bitsandbytes-0.41.1-py3-none-any.whl (92.6 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m19.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: bitsandbytes\n","Successfully installed bitsandbytes-0.41.1\n"]}],"source":["!pip install bitsandbytes"],"id":"gxcYXS_pu0xq"},{"cell_type":"code","execution_count":null,"metadata":{"id":"22kN1QjO1dNG"},"outputs":[],"source":[],"id":"22kN1QjO1dNG"},{"cell_type":"code","execution_count":null,"metadata":{"id":"euhmAZgluhpr"},"outputs":[],"source":["from transformers import BitsAndBytesConfig\n","from peft import prepare_model_for_kbit_training\n","\n","bnb_config = BitsAndBytesConfig(\n","    load_in_4bit=True,\n","    bnb_4bit_use_double_quant=True,\n","    bnb_4bit_quant_type=\"nf4\",\n","    bnb_4bit_compute_dtype=torch.bfloat16\n",")\n","\n","model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path,\n","                                              quantization_config=bnb_config,\n","                                              device_map={\"\":0})\n","\n","model.gradient_checkpointing_enable()\n","model = prepare_model_for_kbit_training(model)\n","model = get_peft_model(model, peft_config)"],"id":"euhmAZgluhpr"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1692029885158,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"w3DDIKpMxHk7","outputId":"f5e0d679-140b-4b06-a3f9-605a045b0456"},"outputs":[{"name":"stdout","output_type":"stream","text":["trainable params: 884,736 || all params: 248,462,592 || trainable%: 0.3560841867092814\n"]}],"source":["model.print_trainable_parameters()"],"id":"w3DDIKpMxHk7"},{"cell_type":"code","execution_count":null,"metadata":{"id":"OzKqzTmb5Zxg"},"outputs":[],"source":[],"id":"OzKqzTmb5Zxg"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":496149,"status":"ok","timestamp":1692029778877,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"04PBY7eruhY_","outputId":"1fc58de9-f9a7-4dd8-dab8-1b3a5ab786f5"},"outputs":[{"name":"stderr","output_type":"stream","text":["  0%|          | 0/121 [00:00<?, ?it/s]`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n","100%|██████████| 121/121 [02:23<00:00,  1.19s/it]\n","100%|██████████| 52/52 [00:20<00:00,  2.59it/s]\n"]},{"name":"stdout","output_type":"stream","text":["epoch=0-> train_loss_avg=tensor(0.3276, device='cuda:0')\t eval_loss_avg=tensor(0.0943, device='cuda:0')\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 121/121 [02:26<00:00,  1.21s/it]\n","100%|██████████| 52/52 [00:20<00:00,  2.60it/s]\n"]},{"name":"stdout","output_type":"stream","text":["epoch=1-> train_loss_avg=tensor(0.1111, device='cuda:0')\t eval_loss_avg=tensor(0.0894, device='cuda:0')\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 121/121 [02:26<00:00,  1.21s/it]\n","100%|██████████| 52/52 [00:20<00:00,  2.60it/s]"]},{"name":"stdout","output_type":"stream","text":["epoch=2-> train_loss_avg=tensor(0.0984, device='cuda:0')\t eval_loss_avg=tensor(0.0839, device='cuda:0')\n"]},{"name":"stderr","output_type":"stream","text":["\n"]}],"source":["device=\"cuda\"\n","model = model.to(device)\n","num_epochs = 3\n","\n","# optimizer and lr scheduler\n","optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n","lr_scheduler = get_linear_schedule_with_warmup(\n","    optimizer=optimizer,\n","    num_warmup_steps=0,\n","    num_training_steps=(len(train_dataloader) * num_epochs),\n",")\n","\n","import time\n","st = time.time()\n","for epoch in range(num_epochs):\n","    model.train()\n","    total_loss = 0\n","    for step, batch in enumerate(tqdm(train_dataloader)):\n","        batch = {k: v.to(device) for k, v in batch.items()}\n","        outputs = model(**batch)\n","        loss = outputs.loss\n","        total_loss += loss.detach().float()\n","        loss.backward()\n","        optimizer.step()\n","        lr_scheduler.step()\n","        optimizer.zero_grad()\n","\n","    model.eval()\n","    eval_loss = 0\n","    eval_preds = []\n","    for step, batch in enumerate(tqdm(eval_dataloader)):\n","        batch = {k: v.to(device) for k, v in batch.items()}\n","        with torch.no_grad():\n","            outputs = model(**batch)\n","        loss = outputs.loss\n","        eval_loss += loss.detach().float()\n","        eval_preds.extend(\n","            tokenizer.batch_decode(\n","                torch.argmax(outputs.logits, -1)\\\n","                .detach().cpu().numpy(),\n","                skip_special_tokens=True)\n","        )\n","    eval_loss_avg = eval_loss / len(eval_dataloader)\n","    train_loss_avg = total_loss / len(train_dataloader)\n","    print(f\"{epoch=}-> {train_loss_avg=}\\t {eval_loss_avg=}\")\n","et = time.time()\n","elapsed_time = et - st"],"id":"04PBY7eruhY_"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":559,"status":"ok","timestamp":1692029784761,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"},"user_tz":-180},"id":"waRFLRBqw7ph","outputId":"0c98a925-4c47-4aa3-b05f-fe991f128a2a"},"outputs":[{"name":"stdout","output_type":"stream","text":["model_name_or_path='google/flan-t5-base'\n","num_epochs=3\n","elapsed_time=495.84 seconds with PEFT\n","Accuracy:0.86\n"]}],"source":["zipped=zip(eval_preds, snli_sampled_dict[\"validation\"][\"label\"])\n","q=[real.strip() in pred.strip() for pred,real in zipped]\n","print(f\"{model_name_or_path=}\")\n","print(f\"{num_epochs=}\")\n","print(f\"{elapsed_time=:.2f} seconds\"\n","     + (\" with PEFT\" if with_peft else  \" without PEFT\"))\n","print(f\"Accuracy:{sum(q)/len(q):.2f}\")"],"id":"waRFLRBqw7ph"},{"cell_type":"code","execution_count":null,"metadata":{"id":"pkLWBW3y1iHp"},"outputs":[],"source":[],"id":"pkLWBW3y1iHp"}],"metadata":{"colab":{"machine_shape":"hm","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.5"},"vscode":{"interpreter":{"hash":"aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"}},"widgets":{"application/vnd.jupyter.widget-state+json":{"03979891ba66453c9a415f25b1179e6b":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_fe471cc2733b46adb2b805e132369bfc","IPY_MODEL_781c8885773a49d38e891001c7c23b59","IPY_MODEL_473332e6cbe448e0b2a399cf139bc950"],"layout":"IPY_MODEL_1dc5f1a40de64c11996362b41e5946ce"}},"076a5938df2b4e89949868d8a63b9b72":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"08f58a4a75ad43e99fce648f453a6f5e":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":null,"overflow_x":null,"overflow_y":null,"padding":null,"right":null,"top":null,"visibility":null,"width":null}},"0c17749ad0bd41289c5b900da553cfc8":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"HBoxModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_5dfdac46ff3246eda8374656f7897e17","IPY_MODEL_276eba4b353a4fde891e9719f86fa792","IPY_MODEL_c79088f8f40e460abe45b6b12dc97b20"],"layout":"IPY_MODEL_cb9baad87be64307aa6293b7bf9136a4"}},"0ffcbd20885d42b2ad2318ed124827db":{"model_module":"@jupyter-widgets/controls","model_module_version":"1.5.0","model_name":"DescriptionStyleModel","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}},"10f6ac2ce3d94a59a75d3a121935dbe0":{"model_module":"@jupyter-widgets/base","model_module_version":"1.2.0","model_name":"LayoutModel","state":{"_model_module":"@jupyter-widgets/base","_model_module_version":"1.2.0","_model_name":"LayoutModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"LayoutView","align_content":null,"align_items":null,"align_self":null,"border":null,"bottom":null,"display":null,"flex":null,"flex_flow":null,"grid_area":null,"grid_auto_columns":null,"grid_auto_flow":null,"grid_auto_rows":null,"grid_column":null,"grid_gap":null,"grid_row":null,"grid_template_areas":null,"grid_template_columns":null,"grid_template_rows":null,"height":null,"justify_content":null,"justify_items":null,"left":null,"margin":null,"max_height":null,"max_width":null,"min_height":null,"min_width":null,"object_fit":null,"object_position":null,"order":null,"overflow":nu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